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Found 8 papers, 1 papers with code
ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent
Answering complex natural language questions often necessitates multi-step reasoning and integrating external information.
Ranked #1 on
Question Answering
on Bamboogle
Large Language Models with Controllable Working Memory
By contrast, when the context is irrelevant to the task, the model should ignore it and fall back on its internal knowledge.
Two-stage LLM Fine-tuning with Less Specialization and More Generalization
Pretrained large language models (LLMs) are general purpose problem solvers applicable to a diverse set of tasks with prompts.
The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers
This paper studies the curious phenomenon for machine learning models with Transformer architectures that their activation maps are sparse.
Understanding Robustness of Transformers for Image Classification
We find that when pre-trained with a sufficient amount of data, ViT models are at least as robust as the ResNet counterparts on a broad range of perturbations.
Modifying Memories in Transformer Models
In this paper, we propose a new task of \emph{explicitly modifying specific factual knowledge in Transformer models while ensuring the model performance does not degrade on the unmodified facts}.
FedMD: Heterogenous Federated Learning via Model Distillation
With 10 distinct participants, the final test accuracy of each model on average receives a 20% gain on top of what's possible without collaboration and is only a few percent lower than the performance each model would have obtained if all private datasets were pooled and made directly available for all participants.
Bootstrapping Mixed Correlators in 4D $\mathcal{N}=1$ SCFTs
The numerical conformal bootstrap is used to study mixed correlators in $\mathcal{N}=1$ superconformal field theories (SCFTs) in $d=4$ spacetime dimensions.
High Energy Physics - Theory
